IEEE journal of biomedical and health informatics
Jul 27, 2021
COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful in...
IEEE journal of biomedical and health informatics
Jul 27, 2021
OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed.
IEEE journal of biomedical and health informatics
Jul 27, 2021
Researchers seek help from deep learning methods to alleviate the enormous burden of reading radiological images by clinicians during the COVID-19 pandemic. However, clinicians are often reluctant to trust deep models due to their black-box character...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jul 24, 2021
Lung cancer is one of the most common and deadly malignant cancers. Accurate lung tumor segmentation from CT is therefore very important for correct diagnosis and treatment planning. The automated lung tumor segmentation is challenging due to the hig...
Computational and mathematical methods in medicine
Jul 20, 2021
In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to ...
OBJECTIVE: To explore the value of a deep learning-based algorithm in detecting Lung CT Screening Reporting and Data System category 4 nodules on chest radiographs from an asymptomatic health checkup population.
Unmasking the decision making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability of patholog...
Journal of medical imaging and radiation oncology
Jul 6, 2021
Deep learning (DL) has shown rapid advancement and considerable promise when applied to the automatic detection of diseases using CXRs. This is important given the widespread use of CXRs across the world in diagnosing significant pathologies, and the...
BACKGROUND: Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretati...
Artificial intelligence, including deep learning, is currently revolutionising the field of medical imaging, with far reaching implications for almost every facet of diagnostic imaging, including patient radiation safety. This paper introduces basic ...